
import numpy as np


import pandas as pd


import matplotlib.pyplot as plt


import seaborn as sns


import plotly.express as px
import plotly.graph_objects as go


iris = pd.read_csv("input/iris/Iris.csv")
iris.head()


age = pd.read_csv("input/titanic/train.csv")
age.head()


cp = pd.read_csv("input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv").fillna(0)
cp.head()


price = pd.read_csv("input/house-prices-advanced-regression-techniques/train.csv")
price.head()


sales = pd.read_csv("input/competitive-data-science-predict-future-sales/items.csv")
sales.head()


stocks = pd.read_csv("input/tesla-stock-data-from-2010-to-2020/TSLA.csv")
stocks.head()


cases = pd.read_csv("input/novel-corona-virus-2019-dataset/covid_19_data.csv")
cases.head()


m = pd.read_csv("input/global-hospital-beds-capacity-for-covid19/hospital_beds_USA_v1.csv")
m.head()


hsp = pd.read_csv("input/global-hospital-beds-capacity-for-covid19/hospital_beds_global_regional_v1.csv")
hsp.head()


word = pd.read_csv("input/nlp-getting-started/train.csv")
word.head()


grs = cp.groupby(["gender"])[["salary"]].mean().reset_index()


px.bar(grs[['gender', 'salary']].sort_values('salary', ascending=False), 
       y="salary", x="gender", color='gender', log_y=True, template='ggplot2',
       width=700, height=500)


px.bar(grs[['gender', 'salary']].sort_values('salary', ascending=False),
       y="gender", x="salary", color='gender', orientation = 'h',
       width=700, height=500)


grgs = cp.groupby(["gender","specialisation"])[["salary"]].mean().reset_index()


px.bar(grgs, x="gender", y="salary", color='specialisation', barmode='stack', width=700, height=500)


px.bar(grgs, x="gender", y="salary", color='specialisation', barmode='group', width=700, height=500)


plt.figure(figsize=(11,7))
sns.countplot(x="gender", data=cp)

<AxesSubplot:xlabel='gender', ylabel='count'>


px.histogram(cp, x="degree_p", y="salary", color="gender", width=700, height=500)


px.density_heatmap(cp, x="degree_p", y="salary", width=700, height=500)


px.density_heatmap(cp, x="degree_p", y="salary",marginal_x="histogram", marginal_y="histogram", width=700, height=500)


px.density_heatmap(cp, x="degree_p", y="salary",facet_row="ssc_b", facet_col="hsc_b", width=700, height=500)


px.histogram(cp, x="degree_p", y="salary", color="gender", marginal="box", width=700, height=500)


px.histogram(cp, x="degree_p", y="salary", color="gender", marginal="violin", width=700, height=500)


plt.figure(figsize=(10,6))
sns.set_style("darkgrid")
sns.kdeplot(data=age['Age'],label="Age" ,shade=True)

<AxesSubplot:xlabel='Age', ylabel='Density'>


sns.displot(age['Age'], bins=10, kde=True, height=7, aspect=1.5)

<seaborn.axisgrid.FacetGrid at 0x25f4cab2f70>


grdsp = cp.groupby(["degree_t"])[["degree_p"]].mean().reset_index()


fig=px.pie(grdsp,
       values="degree_p",
       names="degree_t",
       template="seaborn",
       width=700, height=500)
fig.update_traces(rotation=90, pull=0.05, textinfo="percent+label")


labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500, 2500, 1053, 500]


fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.4)])
fig.update_layout(
    autosize=False,
    width=700,
    height=500)
fig.show()


grss = cp.groupby(["hsc_b","hsc_s"])[["hsc_p"]].mean().reset_index()


fig = px.treemap(grss, path=['hsc_b','hsc_s'], values='hsc_p',
                  color='hsc_p', hover_data=['hsc_s'],
                  color_continuous_scale='rainbow',
                  width=700, height=500)
fig.show()


sales = sales.tail(20)


fig = px.sunburst(sales, path=["item_category_id",'item_id'],
                  color='item_category_id', hover_data=['item_id'],
                  color_continuous_scale='rainbow',
                  width=700, height=500)
fig.show()


plt.figure(figsize=(11,7))
plt.scatter(price.LotArea, price.SalePrice, s=5, alpha=0.5)

<matplotlib.collections.PathCollection at 0x25f4d0163d0>


#Ordinary Least Squares
px.scatter(price, x="LotArea", y="SalePrice",  trendline="ols", trendline_color_override="darkblue",
                  width=700, height=500)


#non-linear Locally Weighted Scatterplot Smoothing
px.scatter(price, x="LotArea", y="SalePrice", trendline="lowess", trendline_color_override="darkblue",
                  width=700, height=500)


px.scatter(price, x="LotArea", y="SalePrice",color="SaleCondition", trendline="ols", width=700, height=500)


sns.lmplot(x = "ssc_p", y= "salary", data=cp)

<seaborn.axisgrid.FacetGrid at 0x25f4efdfe20>


sns.residplot(x=cp.ssc_p, y=cp.salary, lowess=True, color="g")

<AxesSubplot:xlabel='ssc_p', ylabel='salary'>


px.scatter_ternary(cp, a="ssc_p", b="hsc_p",c="degree_p",color = "status",width=700, height=500)


stocks.Date=pd.to_datetime(stocks.Date)


plt.figure(figsize=(13,7))
plt.plot(stocks[:100].Date,stocks[:100].Open,color="black")
plt.plot(stocks[:100].Date,stocks[:100].High,color="red")
plt.plot(stocks[:100].Date,stocks[:100].Low,color="blue")
plt.plot(stocks[:100].Date,stocks[:100].Close,color="Green")
plt.show()


import chart_studio.plotly as py
from bubbly.bubbly import bubbleplot
from plotly.offline import iplot


figure = bubbleplot(dataset=cp.head(30), x_column='hsc_p', y_column='salary', 
                    bubble_column='hsc_b', size_column='salary', color_column='hsc_b', 
                    x_logscale=True, scale_bubble=2, 
                    height=500, width=700)

iplot(figure)


import calmap


all_days = pd.date_range('1/1/2019', periods=700, freq='D')
days = np.random.choice(all_days, 100)
events = pd.Series(np.random.randn(len(days)), index=days)


plt.figure(figsize=(20,10))
calmap.yearplot(events, year=2020)

<AxesSubplot:>


px.box(cp, x="ssc_b", y="ssc_p", color = "specialisation", width=700, height=500)


plt.figure(figsize=(10,6))
sns.violinplot(x="degree_t", y="salary", hue="specialisation", data=cp, palette="muted")

<AxesSubplot:xlabel='degree_t', ylabel='salary'>


plt.figure(figsize=(10,6))
sns.violinplot(x="degree_t", y="salary", hue="specialisation",split = True, data=cp, palette="muted")

<AxesSubplot:xlabel='degree_t', ylabel='salary'>


plt.figure(figsize=(10,6))
sns.swarmplot(x="hsc_s", y="hsc_p", data=cp)

<AxesSubplot:xlabel='hsc_s', ylabel='hsc_p'>


plt.figure(figsize=(10,6))
sns.swarmplot(x="gender", y="hsc_p", data=cp)
sns.boxplot(x="gender", y="hsc_p", data=cp)

<AxesSubplot:xlabel='gender', ylabel='hsc_p'>


plt.figure(figsize=(10,6))
sns.swarmplot(x="gender", y="hsc_p", data=cp)
sns.violinplot(x="gender", y="hsc_p", data=cp)

<AxesSubplot:xlabel='gender', ylabel='hsc_p'>


plt.figure(figsize=(10,6))
sns.stripplot(x="gender", y="hsc_p", data=cp)

<AxesSubplot:xlabel='gender', ylabel='hsc_p'>


plt.figure(figsize=(10,6))
sns.stripplot(x="gender", y="hsc_p", data=cp)
sns.boxplot(x="gender", y="hsc_p", data=cp)

<AxesSubplot:xlabel='gender', ylabel='hsc_p'>


plt.figure(figsize=(10,6))
sns.stripplot(x="gender", y="hsc_p", data=cp)
sns.violinplot(x="gender", y="hsc_p", data=cp)

<AxesSubplot:xlabel='gender', ylabel='hsc_p'>


sns.jointplot(x=cp["mba_p"], y=cp["salary"], kind='scatter')

<seaborn.axisgrid.JointGrid at 0x25f4f5a0c10>


sns.jointplot(x=cp["mba_p"], y=cp["salary"], kind='hex')

<seaborn.axisgrid.JointGrid at 0x25f4f686400>


sns.jointplot(x=cp["mba_p"], y=cp["salary"], kind='kde')

<seaborn.axisgrid.JointGrid at 0x25f4f76f3d0>


fig = go.Figure()

fig.add_trace(go.Funnel(
    name = 'Montreal',
    y = ["Website visit", "Downloads", "Potential customers", "Requested price"],
    x = [120, 60, 30, 20],
    textinfo = "value+percent initial"))

fig.add_trace(go.Funnel(
    name = 'Toronto',
    orientation = "h",
    y = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"],
    x = [100, 60, 40, 30, 20],
    textposition = "inside",
    textinfo = "value+percent previous"))

fig.add_trace(go.Funnel(
    name = 'Vancouver',
    orientation = "h",
    y = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent", "Finalized"],
    x = [90, 70, 50, 30, 10, 5],
    textposition = "outside",
    textinfo = "value+percent total"))

fig.update_layout(
    autosize=False,
    width=1000,
    height=500)

fig.show()


rs = np.random.RandomState(0)
df = pd.DataFrame(rs.rand(10, 10))
df.columns = ['var_' + str(i) for i in df.columns]


corr = df.corr()
corr.style.background_gradient(cmap='coolwarm')


sns.clustermap(corr.corr(), center=0, cmap="vlag", linewidths=.75, figsize=(10, 7))

<seaborn.matrix.ClusterGrid at 0x25f4f778070>


cpp = cp.drop("sl_no", axis=1)


sns.pairplot(cpp)

<seaborn.axisgrid.PairGrid at 0x25f4f9f7280>


cp.plot.area(y=['ssc_p','hsc_p','degree_p','mba_p'], alpha=0.4, figsize=(14, 8));


sl = iris.iloc[:,0]
sw = iris.iloc[:,1]
pl = iris.iloc[:,2]
pw= iris.iloc[:,3]


from matplotlib_venn import venn2
plt.figure(figsize=(10,8))
venn2(subsets = (len(sl)-15, len(sw)-15, 15), set_labels = ('sepal_length', 'sepal_width'))

<matplotlib_venn._common.VennDiagram at 0x25f54685970>


g=sns.FacetGrid(iris, col="Species", height=6)
g.map(sns.kdeplot, "PetalLengthCm")
g.add_legend()

<seaborn.axisgrid.FacetGrid at 0x25f546b5430>


g=sns.FacetGrid(iris, row="Species", height=3)
g.map(sns.kdeplot, "PetalLengthCm")
g.add_legend()

<seaborn.axisgrid.FacetGrid at 0x25f546b5340>


g=sns.FacetGrid(iris, hue="Species", height=6)
g.map(sns.kdeplot, "PetalLengthCm")
g.add_legend()

<seaborn.axisgrid.FacetGrid at 0x25f5488f6a0>


wind = px.data.wind()


px.bar_polar(wind, r="frequency", theta="direction", color="strength", width=700, height=500)


px.line_polar(wind, r="frequency", theta="direction", color="strength",width=700, height=500)


uniform_data = np.random.rand(10, 12)


plt.figure(figsize=(11,8))
sns.heatmap(uniform_data)

<AxesSubplot:>


grp = cases.groupby(['Country/Region'])[['Confirmed', 'Deaths', 'Recovered']].max()
grp = grp.reset_index()
grp['Country'] = grp['Country/Region']


fig = px.choropleth(grp, locations="Country", locationmode='country names', 
                     color="Confirmed", hover_name="Country/Region",hover_data = [grp.Recovered,grp.Deaths],projection="orthographic",
                     color_continuous_scale='Reds', width=700, height=500)
fig.update(layout_coloraxis_showscale=True)


fig = px.choropleth(grp, locations="Country", locationmode='country names', 
                     color="Confirmed", hover_name="Country/Region",hover_data = [grp.Recovered,grp.Deaths],projection="natural earth",
                     color_continuous_scale='Reds',
                 width=700, height=500)
fig.update(layout_coloraxis_showscale=True)


fig = px.choropleth(grp, locations="Country", locationmode='country names', 
                     color="Confirmed", hover_name="Country/Region",hover_data = [grp.Recovered,grp.Deaths],projection="mercator",
                     color_continuous_scale='Reds',
                 width=700, height=500)
fig.update(layout_coloraxis_showscale=True)


fig = go.Figure(go.Densitymapbox(lat=hsp.lat, lon=hsp.lng,radius=10))
fig.update_layout(mapbox_style="stamen-terrain", mapbox_center_lon=0)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()


fig = go.Figure(data=[go.Table(
    header=dict(values=list(grss.columns),
                align='left'),
    cells=dict(values=[grss.hsc_b, grss.hsc_s, grss.hsc_p],
               align='left'))
])

fig.show()


from wordcloud import WordCloud, ImageColorGenerator


text = " ".join(str(each) for each in word.head(2000).keyword.unique())


wordcloud = WordCloud(max_words=200,colormap='Set3', background_color="black").generate(text)
plt.figure(figsize=(15,10))
plt.imshow(wordcloud, interpolation='Bilinear')
plt.axis("off")
plt.figure(1,figsize=(12, 12))
plt.show()


k = m.sort_values(by=['year']).head(200)


fig=px.bar(k,x='state', y="population", animation_frame="year", 
           animation_group="state", color="state", hover_name="state",
           height=500)
fig.update_layout(title='Deaths vs Region')


cds = m.sort_values(by=['year'])
map1  = cds.groupby(['year','state' ,'lat','lng'])['beds'].max()
map1 = map1.reset_index()
map1['size'] = map1['beds']*90000000


fig = px.scatter_mapbox(map1, lat="lat", lon="lng",
                     color="beds", size='size',hover_data=['beds'],
                     color_continuous_scale='burgyl',
                     animation_frame="year",
                     width=700, height=500)
fig.update(layout_coloraxis_showscale=True)
fig.update_layout(mapbox_style="carto-positron", mapbox_zoom=3)
fig.show()


grp = cases.groupby(['ObservationDate', 'Country/Region'])[['Confirmed', 'Deaths', 'Recovered']].max()
grp = grp.reset_index()
grp['Date'] = pd.to_datetime(grp['ObservationDate'])
grp['Date'] = grp['Date'].dt.strftime('%m/%d/%Y')
grp['Active'] = grp['Confirmed'] - grp['Recovered'] - grp['Deaths']
grp['Country'] =  grp['Country/Region']


fig = px.choropleth(grp, locations="Country", locationmode='country names', 
                     color="Active", hover_name="Country/Region",hover_data = [grp.Recovered,grp.Deaths,grp.Active],projection="natural earth",
                     animation_frame="Date",width=800, height=500,
                     color_continuous_scale='Reds',
                     range_color=[1000,100000])
fig.update(layout_coloraxis_showscale=True)
fig.show()


df = px.data.gapminder()


px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country",
           size="pop", color="continent", hover_name="country",
           log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90],
           width=700, height=500)

	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	1	5.1	3.5	1.4	0.2	Iris-setosa
1	2	4.9	3.0	1.4	0.2	Iris-setosa
2	3	4.7	3.2	1.3	0.2	Iris-setosa
3	4	4.6	3.1	1.5	0.2	Iris-setosa
4	5	5.0	3.6	1.4	0.2	Iris-setosa

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

	sl_no	gender	ssc_p	ssc_b	hsc_p	hsc_b	hsc_s	degree_p	degree_t	workex	etest_p	specialisation	mba_p	status	salary
0	1	M	67.00	Others	91.00	Others	Commerce	58.00	Sci&Tech	No	55.0	Mkt&HR	58.80	Placed	270000.0
1	2	M	79.33	Central	78.33	Others	Science	77.48	Sci&Tech	Yes	86.5	Mkt&Fin	66.28	Placed	200000.0
2	3	M	65.00	Central	68.00	Central	Arts	64.00	Comm&Mgmt	No	75.0	Mkt&Fin	57.80	Placed	250000.0
3	4	M	56.00	Central	52.00	Central	Science	52.00	Sci&Tech	No	66.0	Mkt&HR	59.43	Not Placed	0.0
4	5	M	85.80	Central	73.60	Central	Commerce	73.30	Comm&Mgmt	No	96.8	Mkt&Fin	55.50	Placed	425000.0

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	12	2008	WD	Normal	250000

	Date	Open	High	Low	Close	Adj Close	Volume
0	2010-06-29	19.000000	25.00	17.540001	23.889999	23.889999	18766300
1	2010-06-30	25.790001	30.42	23.299999	23.830000	23.830000	17187100
2	2010-07-01	25.000000	25.92	20.270000	21.959999	21.959999	8218800
3	2010-07-02	23.000000	23.10	18.709999	19.200001	19.200001	5139800
4	2010-07-06	20.000000	20.00	15.830000	16.110001	16.110001	6866900

	item_name	item_id	item_category_id
0	! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.) D	0	40
1	!ABBYY FineReader 12 Professional Edition Full...	1	76
2	***В ЛУЧАХ СЛАВЫ (UNV) D	2	40
3	***ГОЛУБАЯ ВОЛНА (Univ) D	3	40
4	***КОРОБКА (СТЕКЛО) D	4	40

	SNo	ObservationDate	Province/State	Country/Region	Last Update	Confirmed
0	1	01/22/2020	Anhui	Mainland China	1/22/2020 17:00	1.0
1	2	01/22/2020	Beijing	Mainland China	1/22/2020 17:00	14.0
2	3	01/22/2020	Chongqing	Mainland China	1/22/2020 17:00	6.0
3	4	01/22/2020	Fujian	Mainland China	1/22/2020 17:00	1.0
4	5	01/22/2020	Gansu	Mainland China	1/22/2020 17:00	0.0

	country	state	county	lat	lng	type	measure	beds	population	year	source	source_url
0	US	AK	aleutians east	63.588753	-154.493062	ICU	1000HAB	0.000000	3338	2019	khn	https://khn.org/news/as-coronavirus-spreads-wi...
1	US	AK	aleutians west	63.588753	-154.493062	ICU	1000HAB	0.000000	5784	2019	khn	https://khn.org/news/as-coronavirus-spreads-wi...
2	US	AK	anchorage	63.588753	-154.493062	ACUTE	1000HAB	2.182916	298225	2018	arcgis	https://services1.arcgis.com/Hp6G80Pky0om7QvQ/...
3	US	AK	anchorage	63.588753	-154.493062	ICU	1000HAB	0.244782	298225	2019	khn	https://khn.org/news/as-coronavirus-spreads-wi...
4	US	AK	anchorage	63.588753	-154.493062	OTHER	1000HAB	0.191131	298225	2019	arcgis	https://services1.arcgis.com/Hp6G80Pky0om7QvQ/...

	country	state	county	lat	lng	type	measure	beds	population	year	source	source_url
0	AD	NaN	NaN	42.50	1.5	ICU	1000HAB	0.071	83747	2011	icm-journal	https://link.springer.com/article/10.1007/s001...
1	AD	NaN	NaN	42.50	1.5	TOTAL	1000HAB	2.500	84463	2009	wdi	https://data.worldbank.org/indicator/SH.MED.BE...
2	AE	NaN	NaN	24.00	54.0	TOTAL	1000HAB	1.200	9197910	2013	wdi	https://data.worldbank.org/indicator/SH.MED.BE...
3	AF	NaN	NaN	33.00	65.0	TOTAL	1000HAB	0.500	34413603	2015	wdi	https://data.worldbank.org/indicator/SH.MED.BE...
4	AG	NaN	NaN	17.05	-61.8	TOTAL	1000HAB	3.800	92562	2014	wdi	https://data.worldbank.org/indicator/SH.MED.BE...

	id	keyword	location	text	target
0	1	NaN	NaN	Our Deeds are the Reason of this #earthquake M...	1
1	4	NaN	NaN	Forest fire near La Ronge Sask. Canada	1
2	5	NaN	NaN	All residents asked to 'shelter in place' are ...	1
3	6	NaN	NaN	13,000 people receive #wildfires evacuation or...	1
4	7	NaN	NaN	Just got sent this photo from Ruby #Alaska as ...	1

	var_0	var_1	var_2	var_3	var_4	var_5	var_6	var_7	var_8	var_9
var_0	1.000000	0.347533	0.398948	0.455743	0.072914	-0.233402	-0.731222	0.477978	-0.442621	0.015185
var_1	0.347533	1.000000	-0.284056	0.571003	-0.285483	0.382480	-0.362842	0.642578	0.252556	0.190047
var_2	0.398948	-0.284056	1.000000	-0.523649	0.152937	-0.139176	-0.092895	0.016266	-0.434016	-0.383585
var_3	0.455743	0.571003	-0.523649	1.000000	-0.225343	-0.227577	-0.481548	0.473286	0.279258	0.446650
var_4	0.072914	-0.285483	0.152937	-0.225343	1.000000	-0.104438	-0.147477	-0.523283	-0.614603	-0.189916
var_5	-0.233402	0.382480	-0.139176	-0.227577	-0.104438	1.000000	-0.030252	0.417640	0.205851	0.095084
var_6	-0.731222	-0.362842	-0.092895	-0.481548	-0.147477	-0.030252	1.000000	-0.494440	0.381407	-0.353652
var_7	0.477978	0.642578	0.016266	0.473286	-0.523283	0.417640	-0.494440	1.000000	0.375873	0.417863
var_8	-0.442621	0.252556	-0.434016	0.279258	-0.614603	0.205851	0.381407	0.375873	1.000000	0.150421
var_9	0.015185	0.190047	-0.383585	0.446650	-0.189916	0.095084	-0.353652	0.417863	0.150421	1.000000

Data Visualization in Python

Matplotlib, Seaborn and Plotly

Catalogue¶

Import libraries¶

Data¶

Bar Plot¶

Vertical Bar¶

Horizontal Bar¶

Stacked Bar¶

Group Bar¶

Count Plot¶

Histogram¶

2d Histogram¶

Marginal Histogram¶

Facet Histogram¶

With Box Margin¶

With Violin Margin¶

Density Plot¶

Pie Plot¶

Donut Plot¶

Tree Plot¶

Sunburst Plot¶

Scatter Plot¶

Trendline¶

Multiple Lines¶

LM Plot¶

Resid Plot¶

Ternary Plot¶

Line Chart¶

Bubble Plot¶

Calendar Plot¶

Box Plot¶

Violin Plot¶

Swarm Plot¶

With Box¶

With violin¶

Strip Plot¶

With Box¶

With violin¶

Joint Plot¶

Funnel Plot¶

Correlation Plot¶

Cluster Map¶

Pair Plot¶

Area Plot¶

Venn Plot¶

Facet Grid¶

Columns¶

Rows¶

HUE¶

Rose Plot¶

Radar Plot¶

Heat Map¶

Choropleth Map¶

Orthographic projection¶

Natural earth projection¶

Mercator projection¶

Density Map¶

Table Plot¶

Word Cloud¶

Animation Plots¶

Bar Plot¶

Scatter Plot¶

Chorolopleth¶

Bubble Plot¶